Jack Williams, iTero and GIANTX: When Artificial Intelligence Steps Into the Esports Coaching Room

A quiet afternoon with no match being broadcast. In an empty newsroom, I reopened my notes on the conversation between Jack Williams and the people behind iTero — an artificial intelligence coaching tool quietly slipping into professional esports. No cheering, no trophy, no fireworks. Just a very dry question: if an algorithm can read a match faster than a human coach, who is really sitting in the coaching chair?

This is not the story of a superstar. It is the story of something that sits at the edge of every match, something crowds never see and cameras never cut to. Like the seventh-place finisher on a running track, these tools also have a name — it is just that nobody has bothered to read it.

Context: an industry asking itself what game it is playing

For more than a decade, esports has moved from amateur tournaments in internet cafes to arenas seating tens of thousands and sponsorships large enough to sustain an ecosystem. But when money enters, questions about the rules enter too. And the newest, hardest, most unprecedented question is this: what is artificial intelligence allowed to do during competitive preparation?

Jack Williams, the central figure in the conversation I am analysing, does not appear as a hero or a villain. He appears as someone trying to sell a product — iTero — in a market where the rules remain ambiguous, and where even his potential customers are not certain whether using it violates anything.

The original article has a striking feature: most of its information is not about the interview subject, but about the writer. That is a hallmark of B2B thought-leadership content aimed at a narrow readership of organisations, investors and managers — not the general public. As a result, the substantive content revolves around only two themes: an exclusivity arrangement with GIANTX and the fear of being copied, and the question of AI-assisted cheating.

These two themes, seemingly separate, are in fact two sides of a larger issue: the commercial and ethical boundaries of coaching tools. One side is money — who gets to use the best tools. The other is law — how far you can go before it becomes cheating. Between them lies a grey zone the industry is still fumbling to map.

Jack Williams and iTero: a product before it is a person

The first thing I notice on rereading the material is how iTero is placed at the centre of the story while Jack Williams himself is almost a narrator. He is not described through competitive achievements, but through something more abstract: a product positioning.

iTero, in its crudest description, is a tool that applies artificial intelligence to analyse competitive data and support a team's preparation. It could belong to the pre-match, mid-series or post-match category — windows where current rules still leave room to negotiate. Critically, it does not intervene directly in real time, because real-time assistance is already clearly prohibited in most major titles. Precisely because it is already clearly prohibited, there is nothing left to debate there. The interesting grey zone lies in the mid-series window — where a tool can offer suggestions between game one and game two of a BO3.

My judgement, after watching a fair number of professional series and witnessing teams flip a tie with a single small adjustment between games, is that the mid-series window is the real battlefield for AI tools, not the game itself. In that brief break, human coaches must process mountains of information under time pressure, and this is exactly where an algorithm can make the biggest difference without breaching any existing prohibition.

But that is precisely why the question of limits matters. If a tool can suggest tactics between games, the line between 'legitimate support' and 'coaching by machine' becomes so thin it is hard to define in legal text. And when the law cannot define it, the industry usually chooses silence — until someone exploits it to a point where silence is no longer possible.

GIANTX and the exclusivity deal: money goes first, law follows

The most important part of the original article concerns iTero working exclusively with GIANTX. This is the point I believe deserves deeper analysis than any other, because it touches a question European esports has never had to answer seriously: does an exclusive tooling deal create an unfair competitive advantage inside a closed league?

GIANTX, per general industry understanding, is an organisation rooted in the merger of two EMEA entities and tied to the League of Legends ecosystem, specifically the LEC — Riot Games' regional league. If that is accurate, the legal framework governing the iTero arrangement is Riot's third-party software and competitive-integrity rulebook.

There is a very important structural feature here. The LEC operates as a closed franchise, meaning all participating teams are permanent members with no relegation pressure. In such a system, a structural advantage — such as exclusive access to an analytics tool — persists across seasons rather than being competed away. This makes exclusivity far more consequential than in open systems, where weaker teams must improve or be removed.

In other words, in a franchised league, tooling exclusivity is not merely a temporary edge. It is a permanent differential written into the system.

This also raises a governance question for the publisher-cum-league-operator. If a tool can materially affect competitive outcomes, the operator will soon have to choose: either force equal access for all teams, or restrict the tool itself. History has shown this pattern — how in-game coach communication was progressively tightened season by season — and I believe AI tools will follow a similar trajectory.

The fear of being copied and the story of commoditisation

Another part of the original article touches on the possibility of iTero being copied. This is a very real fear for any technology company operating in an industry where processes can be copied, reverse-engineered or replicated if the barriers are not solid.

Here, I want to be blunt: in esports, almost everything can be commoditised. Data analysis can be copied. Machine learning models can be rebuilt if the copier has enough data. Coaching processes can be spied on during scrims.

But the issue runs deeper. The true value of a tool like iTero is not the algorithm — it is the contract: exclusivity and data access. The algorithm can be copied, but an exclusivity agreement with a leading organisation cannot. That is why tooling deals increasingly resemble sponsorship deals rather than software purchases.

And this has a paradoxical consequence: if iTero succeeds, its value will not lie in the product, but in the structure of relationships. If iTero fails, it may be because that very structure was broken by a competitor first.

AI-assisted cheating: a question without an answer

The second part of the original article touches on AI-assisted cheating. This is the hardest part to analyse, because it requires defining something that current definitions have not caught up with.

What is cheating? In traditional esports, cheating has a relatively clear definition: using illegal software, colluding with outsiders, interfering with results. But when artificial intelligence suggests tactics before a match, is that cheating? If a coach reads a report generated by AI and adjusts tactics accordingly, what is he doing wrong?

The honest answer is: we do not know. And I believe that very 'not knowing' is the problem, not any particular behaviour.

In this context, I believe the industry's greatest mistake will not be allowing AI to do too much, but allowing AI to do too little while pretending to be in control. Ambiguous prohibitions create two kinds of teams: those who comply strictly and fall behind, and those who exploit the grey zone and move ahead. In the long run, it is the ambiguity that distorts competition, not the technology.

One technical detail also deserves note: most current discussion of AI in esports concerns the between-games window, not real-time assistance. This is an important observation, because it narrows the problem to a specific time window — the moment when a human coach makes the biggest decisions with the least synthesised information.

A good AI tool can, within minutes, aggregate data from the just-finished game, cross-reference thousands of similar historical situations and propose a direction. This is good for viewers — because the match is better adjusted — but it also raises a philosophical question: if the coach is only reading back what the algorithm said, where does human skill lie?

Valve and Riot: two governance philosophies, one divided industry

One thing I have always found fascinating about the major titles is how the two leading publishers — Valve and Riot Games — differ on allowing third-party tools. This difference is not merely philosophical; it directly shapes the addressable market for tooling vendors like iTero.

Dota 2, under Valve, has large but infrequent update cycles. Big systemic patches arrive and then leave long stretches of stability. This means machine learning models trained on historical data retain value for longer windows. In such an environment, statistical and machine learning tools have the advantage.

Conversely, League of Legends under Riot has a fast cadence, often a patch every two weeks. This shortens the lifespan of any pattern learned from the past, and shifts the core value of an AI tool: from 'solving the meta' to 'detecting the meta shift faster than opponents'.

In the first case, it is a knowledge advantage. In the second, it is a tempo advantage.

This is not a small technical detail. If a single AI product is marketed identically across both titles, that is a red flag. Because the tool's true value inverts between the two environments: slow-cadence leagues reward depth of historical modelling, while fast-cadence leagues reward speed of reaction.

I believe this is one of the most overlooked angles of the iTero story — not what the tool has, but which title it fits, and whether it is trying to satisfy all of them at once.

The difference between the two titles and the truth about versatility

One of the most easily overlooked things when discussing AI tools in esports is the assumption that 'a good tool works for every title'. That is an extremely dangerous assumption.

Different titles differ not only in mechanics, but in the speed at which rules change, the volume of public data, and the regulations on competitive servers. A tool optimised for Dota 2 may be useless in an environment where patches change constantly and professional match data is locked away.

Moreover, how a team uses data differs. Some teams have deep pre-match analysis habits, while others rely more on in-the-moment reactions. A good tool must fit the team's culture, not just the title.

This brings me to a conclusion that is not pleasant to hear: the product-performance claims for iTero in the original article are, evidentially, unverifiable. No data, no sample size, no evaluation methodology is disclosed. As someone whose profession requires verification before writing, I must be clear: we do not know how effective this product is.

And in that context, every claim of effectiveness should be treated as a marketing claim until independent evidence exists.

What the article does not say: the data gap and the trap of silence

There is an irony I noticed while analysing the source: the more I read, the more the article seemed to be about the writer than the subject. Almost all source information points revolve around the reporter's biography, not the interview content. This is a trap I recognise I have fallen into myself — the silence of data sometimes pushes writers to fill it with themselves.

I call this the trap of silence. When there is not enough evidence to tell the real story, writers tend to tell the story of themselves instead. But that way is not honest. The honest way is to say clearly: we do not know. We only know that there is an exclusivity deal, a fear of copying, and an unanswered cheating question.

Those three facts, added together, are already enough to tell a story. No embellishment needed.

A counterintuitive angle: when transparency becomes a disadvantage

Here I want to offer the most counterintuitive angle I see in this whole story.

We tend to think transparency in esports is an absolute value — that all teams should have equal access, that all tools should be public and verifiable. But I fear that in the specific context of a closed franchised league, transparency can become a competitive disadvantage for the very teams that want to comply.

Imagine: a team that strictly follows the rules, using no AI assistance, then loses to a team that exploited the grey zone. The compliant team loses points, prize money, opportunity. Meanwhile the non-compliant team is called 'smart'.

The result is that so-called transparency in the law creates an implicit reward for those who are non-transparent in behaviour. This is a paradox I believe any industry administrator must confront.

The only way out, in my view, is not more prohibition, but clearer definition. A document is needed that states plainly: what AI is allowed to do in which time window, at what level of intervention, and who is responsible when the line is crossed. Right now, the industry is trying to govern a new technology using rules written for an old one.

Money, contracts and the structure behind every claim

In analyses of transfer windows and technology deals, I always start with structural questions: where the money flows, who holds control, and which clause determines the end of the relationship.

With iTero and GIANTX, the question is whether the exclusivity deal comes with data clauses. In the technology industry, data is the real asset. On the surface, iTero sells an analytics tool. But if the exclusivity deal includes access to GIANTX match data, then the real asset is not the software, but the exclusive data trove no rival can obtain.

That is a very clever business model — and also a very fragile one. Because when the exclusive contract expires, the value may vanish with it.

In sport generally, we have seen data deals worth enormous sums repriced after a single season. The same could happen to AI tools in esports.

What audiences see and do not see

One aspect I always think about is the viewer experience. Watching a professional match, viewers see two teams facing off, see a coach called on stage to shake hands, but do not see what is really happening behind the scenes. They do not know that during the break between two games, an algorithm might be running and offering suggestions.

There is a line I always carry when writing: if football were only numbers, we would not need the stands. What makes a grand final valuable is not the data, but the human ability to decide under pressure, and the human ability to err. It is precisely the human error that makes the story worth telling.

So if AI can reduce the probability of human error, is it impoverishing the sporting story? This is an open question, and I admit I have no certain answer. But I think it is a question anyone who loves sport should ask.

When technology becomes a commodity and the seventh-place finisher is left behind

In previous articles I have often spoken of the concept of commoditisation in sport: what happens when a sport becomes a tradable good, when clubs become assets, when players become assets. Technology does not escape that law. When an AI tool becomes central to the game, it becomes a commodity — something only rich teams can buy.

And here I think again of the seventh-place finishers. Teams that cannot afford the best tools. Young players with no access to expensive analytics. Amateur coaches with no chance to compete against teams equipped with technological weapons.

Sporting history shows that tooling inequality never disappears on its own. It is only controlled when rules are clear. But here the rules lag technology by a long stride.

What happens next: three possible scenarios

At this point, I think prediction is necessary, but prediction must be based on existing evidence, not inspiration. With what we know, I can imagine three scenarios.

The first is clear legalisation. Valve and Riot issue specific rules on AI: banning real-time assistance, permitting pre-match analysis, defining team responsibility when using tools with automatic suggestions. This is the cleanest scenario, but also the least feasible, because it requires cooperation among many parties.

The second is industry self-regulation. Teams and organisations agree among themselves on a common standard, without publisher intervention. This is the most common scenario in esports history, and often collapses when one party feels wronged.

The third — and the one I fear most — is indistinction. No clear rules, parties interpret the rules in their own favour, and the game quietly becomes unfair. This is the scenario where AI tools are neither banned nor controlled, and those who comply are the losers.

Based on my experience watching matches and rule debates in the industry, I believe the second scenario will appear first, but the third will last the longest.

On iTero, GIANTX and what remains of the story

Returning to iTero and GIANTX, I believe this story will continue in a far quieter way than the public imagines. Tooling deals mostly happen behind contracts journalists never read. Rule changes are mostly issued in meetings audiences never know about.

And here, I suddenly think of something I always stress: seventh-place finishers also have a name on the running track. In this story, the seventh-place finisher may be a small team that cannot afford the best tool. It may be a coach with no budget. It may be a young player with no chance to compete fairly.

I write this partly for them. For those trapped between an industry changing its rules without saying clearly who gains and who loses.

The necessary silence

In interviews, I rarely ask about the feeling of winning. I prefer to ask about the silence after a loss. Likewise, in analysis, I believe data gaps are not things to be filled with speculation, but things to be respected.

The original article has many gaps. It does not say who Jack Williams is beyond his association with iTero. It does not say what iTero specifically does. It does not say how long the GIANTX exclusivity deal lasts, or its value. It does not say whether any third party is competing.

These are gaps I refuse to fill with speculation. And I believe acknowledging them is as important as analysing what is present.

Numbers are the ashes of the match

Throughout my writing career, I have always believed that numbers in a stats table are the ashes of the match. They are the traces of decisions already broken, moments already over. In the story of AI in esports, we are at a stage where the ashes have not yet settled.

Perhaps in a few years we will look back and see that the 2026 period was when the line between human coach and machine tool began to blur. Perhaps that is good. Perhaps it is bad. But what is certain is that it will not arrive on its own — it will arrive from the decisions of a small group of people sitting in a closed meeting room.

Facing the unknown

What I take from this whole analytical process is a lesson in humility. Facing a new topic — AI in esports coaching — writers are easily tempted by decisive conclusions. But honesty demands we admit there are things we do not know.

I do not know whether iTero will succeed. I do not know whether GIANTX is at a disadvantage by signing an exclusive deal. I do not know how publishers will react. But I know one thing: the debate about AI in esports is beginning, and it will not end soon.

And that is why I write this — not to provide answers, but to pose the right question.

Those left behind the camera

In any technological change, there are always those left behind. In this case, they are coaches untrained to work with data. Teams with no dedicated analyst. Players who play well but cannot read a report.

When an entire industry chases a new tool, those who cannot keep up are pushed to the margins. And over time, the margins become home to many.

I write these lines not to oppose technology. I write to remind that technology always has winners and losers, and the losers usually have no voice.

Looking back at the conversation with Jack Williams

If there is one thing I would ask Jack Williams that the original article does not answer, it is: what does he think about a technology company's responsibility in shaping the rules of a sport? This is a question I believe every founder of a sporting tool should ask themselves.

Because when a tool can change how a team prepares, that tool is no longer a neutral product. It is part of the rules of the game. And the rulemaker, whether they like it or not, must bear responsibility for what they create.

What is not said in the two headings

The original article mentions two section headings: one on working exclusively with GIANTX and the likelihood of being copied, one on AI-assisted cheating. Both matter. But there is a third analytical frame I believe was left empty: league fairness.

Between the commercial story and the ethical story, there is an institutional story. About whether a league should allow one member a tooling advantage others lack. This is a question I am certain will be asked again, and probably at a conference nobody outside the industry notices.

On the value of slowness

Finally, I want to speak of something I deeply believe: the value of slowness in an industry running too fast. AI arrives, new tools arrive, new rules arrive, but the question about human beings remains old. Do we want esports to be a sport where human skill is celebrated, or one where the best tool wins?

This is not a rhetorical question. It is a question that must be answered with action — with specific regulations, transparent agreements, public debate.

And I, as a writer, will keep recording everything I see. Even when what I see is only a silence in a closed meeting room, with no camera, no audience, no applause.

Because sometimes the most important decisions in a sport are made where nobody is watching. And the seventh-place finisher also has a name on the running track — even when that track is now measured by algorithms.

I still remember the feeling in 2026, when stadiums stood empty and I sat alone with data. I heard a match breathe in that empty stadium. That was when I understood that sport is not in the numbers, but in the silence between them. And in the story of AI in esports, that silence is growing wider.

The track is measured in seconds, but the pain is measured in years. And in the case of small teams without the best tools, that pain will outlast an entire tournament cycle.

That is why I write. Not to predict the future, but to record the present before it is rewritten by an algorithm.